Interpretable predictive value of including HDL-2b and HDL-3 in an explainable boosting machine model for multiclass classification of coronary artery stenosis severity in acute myocardial infarction patients.

ABSTRACT:

Aims

The aim of this study was to use explainable boosting machine (EBM) to evaluate the predictive value of HDL-2b and HDL-3 levels in comparison with traditional lipid parameters in three-class classification of coronary artery stenosis severity in acute myocardial infarction (AMI) patients.

Methods and results

In this cross-sectional study, 1200 AMI patients were evaluated. HDL subtypes were quantified via microfluidic chip detection, and stenosis severity was assessed via the Gensini scoring system. The Gensini scores were divided into three groups: low group (<36.5), moderate group (36.5-72), and high group (>72). Explainable boosting machine, an interpretable machine learning technique, was employed to assess the predictive value of HDL-2b and HDL-3 compared with tradi

SUBMITTER: Wang B 

PROVIDER: S-EPMC11914717 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature